Anomaly Detection

Anomalies are patterns in the data that do not conform to a well-defined notion of normal behaviour.

Techniques used to detection anomalies typically require training before using on new data.

This Jupyter Notebook reproduces the results from Oana Niculaescu's article in XRDS, Applying Data Science for Anomaly and Change Point Detection.

Build

To start Jupyter Notebook in background (will open browser):

nice jupyter-notebook &

To stop Jupyter notebooks:

jupyter-notebook stop

Output

To output a notebook as PDF or LaTeX:

jupyter nbconvert --exec --to latex --template article.tplx [your-notebook.ipynb]

Customise

To further customise output of a notebook, see

• NB Format (PDF)
• Customizing nbconvert (blog)